Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
2. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 21 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Moderator_Qualcomm (3GPP TSG-RAN WG4 Meeting #108, R4-2314222, “Topic summary for FS_NR_AIML_air” dated Aug, 2023) in view of LI et al. (US Pub. No: 2025/0192842 A1) and further in view of XUE et al. (US Pub. No: 2021/0376895 A1).
Regarding claim 21, Moderator_Qualcomm teaches an apparatus configured as test equipment for over-the-air (OTA) conformance testing of model-based channel state information prediction (see page 1, Introduction & page 30, Fig.5, TE/apparatus and also see page 46, Fig.2), comprising: a channel emulator coupled to at least one test probe positioned in a test chamber (see page 30, Fig.5, Scenario generator/channel emulator coupled to test signaling generator/test probe inside TE, is mentioned);
at least one processor and at least one memory storing instructions that (see page 30, Fig.5, Test controller/processor along with Model functionality monitoring/control block/memory inside TE, is mentioned), when executed by the at least one processor, cause the apparatus at least to (see page 30, Fig.5): transmit, to a device under test comprising a user equipment (UE), a first indication to disable an artificial intelligence/machine learning (AI/ML) feature that performs channel state information (CSI) prediction (see page 30, Fig.5, wherein LCM signaling (that includes first indication to disable an AI/ML feature) being sent from TE to DUT/UE/device_under_test, is mentioned and also see page 14, under section 1.2.11 sub-topic 1-11 for Model deactivation, wherein disabling an AI/ML model for AI/ML enabled feature, is mentioned);
receive, from the UE, an acknowledgment confirming that the AI/ML feature has been disabled (see page 30, Fig.5 and also see page 14, under section 1.2.11 sub-topic 1-11 for Model deactivation, wherein disabling an AI/ML model for AI/ML enabled feature, is mentioned and also see page 20, proposal 4);
configure the UE to measure and report CSI using non-AI/ML functionality (see 36, observation 12, wherein performing non-AI models/non-AI algorithms, is mentioned and also see page 29, under issue 3, wherein number of tests per test set up including CSI configuration, is mentioned and also see page 30, Fig.5); determine a number of ground-truth CSI reports (see page 30, Fig.5 wherein TE-side monitoring with ground-truth CSI reporting, is mentioned);
after completing a ground-truth table, transmit, to the UE, a second indication to enable the AI/ML feature (see page 30, Fig.5, wherein LCM signaling (that includes also second indication to enable an AI/ML feature) being sent from TE to DUT/UE/device_under_test, is mentioned and also see page 13, under section 1.2.11 for Model activation, enabling an AI/ML model for AI/ML enabled feature, is mentioned); receive, from the UE, an acknowledgment confirming that the AI/ML feature has been enabled (see page 30, Fig.5, and also see page 13, under section 1.2.11 for Model activation, enabling an AI/ML model for AI/ML enabled feature, is mentioned and also see page 20, proposal 4) and configure the UE to predict and report CSI using the AI/ML feature (see page 20, proposal 4, wherein for AI/ML based feedback, performance requirement including CSI-RS measurement reports, is mentioned and also see page 29, under issue 3, wherein number of tests per test set up including CSI configuration, is mentioned and also see page 30, Fig.5).
Moderator_Qualcomm is silent in teaching the above apparatus comprising determining a plurality of sets of channel parameters having a number of sets corresponding to the number of ground-truth CSI reports, each set of channel parameters defining a respective channel condition, during a first phase, for each set of channel parameters of the plurality of sets of channel parameters, configure the channel emulator according to the set of channel parameters, transmit channel state information reference signals (CSI-RSs) to the UE, receive from the UE a measured CSI report comprising a measured CSI value corresponding to the set of channel parameters, and store the set of channel parameters and the measured CSI value in a ground- truth table, during a second phase that is non-overlapping with the first phase, configure the channel emulator using the same plurality of sets of channel parameters, receive from the UE a predicted CSI report comprising a predicted CSI value corresponding to each respective set of channel parameters and store each set of channel parameters and the corresponding predicted CSI value in a prediction table.
However, LI et al. teach an apparatus (see Abstract and Figures 2 & 6, network entity 105-a/105-c) comprising determining a plurality of sets of channel parameters having a number of sets corresponding to the number of ground-truth CSI reports, each set of channel parameters defining a respective channel condition (see para [0109] wherein the network entity 105-b determining/transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, the configuration 210 configuring the UE 115-a with a CSI report setting group 220, which includes a first subset of CSI report settings (e.g., a CSI report setting 225) associated with measured channel characteristics of a first CMR set 250 and a second subset of CSI report settings (e.g., CSI report setting 230-a or CSI report setting 230-b) associated with channel characteristics of a second CMR set 255, is mentioned),
during a first phase, for each set of channel parameters of the plurality of sets of channel parameters, configure the channel emulator according to the set of channel parameters (see para [0109] wherein the network entity 105-b determining/transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, the configuration 210 configuring the UE 115-a with a CSI report setting group 220, which includes a first subset of CSI report settings (e.g., a CSI report setting 225) associated with measured channel characteristics of a first CMR set 250 and a second subset of CSI report settings (e.g., CSI report setting 230-a or CSI report setting 230-b) associated with channel characteristics of a second CMR set 255, is mentioned and also see para [0113]), transmit channel state information reference signals (CSI-RSs) to the UE (see para [0109] wherein the network entity 105-b transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, is mentioned and also see para [0113]),
receive from the UE a measured CSI report comprising a measured CSI value corresponding to the set of channel parameters and store the set of channel parameters and the measured CSI value in a ground- truth table (see Fig.6 and para [0117] wherein the UE 115-a transmitting multiple CSI reports based on the CSI report settings & for example, the UE 115-a may transmit a first CSI report that indicates one or more measured channel characteristics 240 of the first CMR set 250 based on the first CSI report setting configuration 210, is mentioned and also see para [0118]),
during a second phase that is non-overlapping with the first phase, configure the channel emulator using the same plurality of sets of channel parameters (see para [0109] wherein the network entity 105-b determining/transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, the configuration 210 configuring the UE 115-a with a CSI report setting group 220, which includes a first subset of CSI report settings (e.g., a CSI report setting 225) associated with measured channel characteristics of a first CMR set 250 and a second subset of CSI report settings (e.g., CSI report setting 230-a or CSI report setting 230-b) associated with channel characteristics of a second CMR set 255, is mentioned & also the second CMR set 255 may be associated with the first CMR set 250 or may be the same as the first CMR set 250 (that includes configuring the channel emulator using the same plurality of sets of channel parameters), is mentioned and also see para [0113]), receive from the UE a predicted CSI report comprising a predicted CSI value corresponding to each respective set of channel parameters and store each set of channel parameters and the corresponding predicted CSI value in a prediction table (see Fig.6 and para [0117] wherein the UE 115-a separately transmitting multiple CSI reports based on the CSI report settings, is mentioned and also the UE 115-a transmitting a second CSI report that indicates one or more predicted channel characteristics 245 of the second CMR set 255 based on the second CSI report setting configuration 210, is mentioned and also see para [0118]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above apparatus of Moderator_Qualcomm to include all of determining a plurality of sets of channel parameters having a number of sets corresponding to the number of ground-truth CSI reports, each set of channel parameters defining a respective channel condition, during a first phase, for each set of channel parameters of the plurality of sets of channel parameters, configuring the channel emulator according to the set of channel parameters, transmit channel state information reference signals (CSI-RSs) to the UE, receiving from the UE a measured CSI report comprising a measured CSI value corresponding to the set of channel parameters and storing the set of channel parameters and the measured CSI value in a ground- truth table, during a second phase that is non-overlapping with the first phase, configuring the channel emulator using the same plurality of sets of channel parameters, receiving from the UE a predicted CSI report comprising a predicted CSI value corresponding to each respective set of channel parameters and storing each set of channel parameters and the corresponding predicted CSI value in a prediction table, disclosed by LI et al. in order to provide an effective mechanism of a wireless device for efficiently configuring a user equipment (UE) with multiple CSI report settings and also configuring UE to perform CSI reporting of measured and predicted channel characteristics in the time domain, spatial domain, frequency domain, or any combination thereof, in accordance with a corresponding CSI report setting in wireless communication system.
Moderator_Qualcomm and LI et al. together yet are silent in teaching the above apparatus comprising determining a prediction accuracy of the AI/ML feature by comparing, for each set of channel parameters, the measured CSI value and the predicted CSI value corresponding to the same set of channel parameters.
However, XUE et al. teach an apparatus (see Abstract and Fig.8/gNB) comprising determining a prediction accuracy of the AI/ML feature by comparing, for each set of channel parameters, the measured CSI value and the predicted CSI value corresponding to the same set of channel parameters (see Fig.4 and para [0050] wherein a machine learning (ML) based (CSI prediction) model may be trained to learn relationships between measured CSI at a first time, measured CSI at a second time, and the time delay between the first time and the second time and CSI measurements being performed at the UE and reported to the gNB, is mentioned and also these measurements being added to a training data set that may be used to train (or re-train) the ML-based CSI prediction model, is mentioned and also see Fig.9, see para [0094] wherein the UE measuring CSI based on the reference signals 912 and predicting CSI at a future time using the prediction model, is mentioned & para [0095] wherein the UE 902 transmitting a message 920 including the predicted CSI and measured CSI reports to gNB 904 for use by the gNB to configure a qualifying scheme for the UE, is mentioned and also see para [0096] wherein the gNB determining whether the differences reported by the UE indicate that the respective CSI predictions are qualified or nonqualified predictions and after determining whether the predictions are qualified or nonqualified predictions, and gNB 904 generating the qualifying scheme configuration (that includes determining a prediction accuracy of the AI/ML feature), is mentioned).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above apparatus of Moderator_Qualcomm and LI et al. to include determining a prediction accuracy of the AI/ML feature by comparing, for each set of channel parameters, the measured CSI value and the predicted CSI value corresponding to the same set of channel parameters, disclosed by XUE et al. in order to provide an effective mechanism of a wireless device for efficiently predicting channel state information (CSI) using machine learning models and qualifying CSI predictions made using machine learning models and also efficiently reporting to o the network entity both the calculated CSI and the quantized CSI difference value in wireless communication system.
Regarding claim 31, Moderator_Qualcomm teaches a method performed by test equipment for over-the-air (OTA) conformance testing of model-based channel state information prediction (see page 1, Introduction & page 30, Fig.5, TE/test equipment and also see page 46, Fig.2) using a channel emulator coupled to at least one test probe positioned in a test chamber (see page 30, Fig.5, Scenario generator/channel emulator coupled to test signaling generator/test probe inside TE, is mentioned), the method comprising: transmitting, to a device under test comprising a user equipment (UE), a first indication to disable an artificial intelligence/machine learning (AI/ML) feature that performs channel state information (CSI) prediction (see page 30, Fig.5, wherein LCM signaling (that includes first indication to disable an AI/ML feature) being sent from TE to DUT/UE/device_under_test, is mentioned and also see page 14, under section 1.2.11 sub-topic 1-11 for Model deactivation, wherein disabling an AI/ML model for AI/ML enabled feature, is mentioned);
receiving, from the UE, an acknowledgment confirming that the AI/ML feature has been disabled (see page 30, Fig.5 and also see page 14, under section 1.2.11 sub-topic 1-11 for Model deactivation, wherein disabling an AI/ML model for AI/ML enabled feature, is mentioned and also see page 20, proposal 4);
configuring the UE to measure and report CSI using non-AI/ML functionality (see 36, observation 12, wherein performing non-AI models/non-AI algorithms, is mentioned and also see page 29, under issue 3, wherein number of tests per test set up including CSI configuration, is mentioned and also see page 30, Fig.5); determining a number of ground-truth CSI reports (see page 30, Fig.5 wherein TE-side monitoring with ground-truth CSI reporting, is mentioned);
after completing a ground-truth table, transmitting, to the UE, a second indication to enable the AI/ML feature (see page 30, Fig.5, wherein LCM signaling (that includes also second indication to enable an AI/ML feature) being sent from TE to DUT/UE/device_under_test, is mentioned and also see page 13, under section 1.2.11 for Model activation, enabling an AI/ML model for AI/ML enabled feature, is mentioned); receiving, from the UE, an acknowledgment confirming that the AI/ML feature has been enabled (see page 30, Fig.5, and also see page 13, under section 1.2.11 for Model activation, enabling an AI/ML model for AI/ML enabled feature, is mentioned and also see page 20, proposal 4) and configuring the UE to predict and report CSI using the AI/ML feature (see page 20, proposal 4, wherein for AI/ML based feedback, performance requirement including CSI-RS measurement reports, is mentioned and also see page 29, under issue 3, wherein number of tests per test set up including CSI configuration, is mentioned and also see page 30, Fig.5).
Moderator_Qualcomm is silent in teaching the above method comprising determining a plurality of sets of channel parameters having a number of sets corresponding to the number of ground-truth CSI reports, each set of channel parameters defining a respective channel condition, during a first phase, for each set of channel parameters of the plurality of sets of channel parameters, configure the channel emulator according to the set of channel parameters, transmit channel state information reference signals (CSI-RSs) to the UE, receive from the UE a measured CSI report comprising a measured CSI value corresponding to the set of channel parameters, and store the set of channel parameters and the measured CSI value in a ground- truth table, during a second phase that is non-overlapping with the first phase, configure the channel emulator using the same plurality of sets of channel parameters, receive from the UE a predicted CSI report comprising a predicted CSI value corresponding to each respective set of channel parameters and store each set of channel parameters and the corresponding predicted CSI value in a prediction table.
However, LI et al. teach a method (see Abstract and Figures 2 & 6, network entity 105-a/105-c) comprising determining a plurality of sets of channel parameters having a number of sets corresponding to the number of ground-truth CSI reports, each set of channel parameters defining a respective channel condition (see para [0109] wherein the network entity 105-b determining/transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, the configuration 210 configuring the UE 115-a with a CSI report setting group 220, which includes a first subset of CSI report settings (e.g., a CSI report setting 225) associated with measured channel characteristics of a first CMR set 250 and a second subset of CSI report settings (e.g., CSI report setting 230-a or CSI report setting 230-b) associated with channel characteristics of a second CMR set 255, is mentioned),
during a first phase, for each set of channel parameters of the plurality of sets of channel parameters, configure the channel emulator according to the set of channel parameters (see para [0109] wherein the network entity 105-b determining/transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, the configuration 210 configuring the UE 115-a with a CSI report setting group 220, which includes a first subset of CSI report settings (e.g., a CSI report setting 225) associated with measured channel characteristics of a first CMR set 250 and a second subset of CSI report settings (e.g., CSI report setting 230-a or CSI report setting 230-b) associated with channel characteristics of a second CMR set 255, is mentioned and also see para [0113]), transmit channel state information reference signals (CSI-RSs) to the UE (see para [0109] wherein the network entity 105-b transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, is mentioned and also see para [0113]),
receive from the UE a measured CSI report comprising a measured CSI value corresponding to the set of channel parameters and store the set of channel parameters and the measured CSI value in a ground- truth table (see Fig.6 and para [0117] wherein the UE 115-a transmitting multiple CSI reports based on the CSI report settings & for example, the UE 115-a may transmit a first CSI report that indicates one or more measured channel characteristics 240 of the first CMR set 250 based on the first CSI report setting configuration 210, is mentioned and also see para [0118]),
during a second phase that is non-overlapping with the first phase, configure the channel emulator using the same plurality of sets of channel parameters (see para [0109] wherein the network entity 105-b determining/transmitting control signaling indicating a configuration 210 for the multiple CSI report settings to the UE 115-a, the configuration 210 configuring the UE 115-a with a CSI report setting group 220, which includes a first subset of CSI report settings (e.g., a CSI report setting 225) associated with measured channel characteristics of a first CMR set 250 and a second subset of CSI report settings (e.g., CSI report setting 230-a or CSI report setting 230-b) associated with channel characteristics of a second CMR set 255, is mentioned & also the second CMR set 255 may be associated with the first CMR set 250 or may be the same as the first CMR set 250 (that includes configuring the channel emulator using the same plurality of sets of channel parameters), is mentioned and also see para [0113]), receive from the UE a predicted CSI report comprising a predicted CSI value corresponding to each respective set of channel parameters and store each set of channel parameters and the corresponding predicted CSI value in a prediction table (see Fig.6 and para [0117] wherein the UE 115-a separately transmitting multiple CSI reports based on the CSI report settings, is mentioned and also the UE 115-a transmitting a second CSI report that indicates one or more predicted channel characteristics 245 of the second CMR set 255 based on the second CSI report setting configuration 210, is mentioned and also see para [0118]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above method of Moderator_Qualcomm to include all of determining a plurality of sets of channel parameters having a number of sets corresponding to the number of ground-truth CSI reports, each set of channel parameters defining a respective channel condition, during a first phase, for each set of channel parameters of the plurality of sets of channel parameters, configuring the channel emulator according to the set of channel parameters, transmit channel state information reference signals (CSI-RSs) to the UE, receiving from the UE a measured CSI report comprising a measured CSI value corresponding to the set of channel parameters and storing the set of channel parameters and the measured CSI value in a ground- truth table, during a second phase that is non-overlapping with the first phase, configuring the channel emulator using the same plurality of sets of channel parameters, receiving from the UE a predicted CSI report comprising a predicted CSI value corresponding to each respective set of channel parameters and storing each set of channel parameters and the corresponding predicted CSI value in a prediction table, disclosed by LI et al. in order to provide an effective mechanism of a wireless device for efficiently configuring a user equipment (UE) with multiple CSI report settings and also configuring UE to perform CSI reporting of measured and predicted channel characteristics in the time domain, spatial domain, frequency domain, or any combination thereof, in accordance with a corresponding CSI report setting in wireless communication system.
Moderator_Qualcomm and LI et al. together yet are silent in teaching the above method comprising determining a prediction accuracy of the AI/ML feature by comparing, for each set of channel parameters, the measured CSI value and the predicted CSI value corresponding to the same set of channel parameters.
However, XUE et al. teach a method (see Abstract and Fig.8/gNB) comprising determining a prediction accuracy of the AI/ML feature by comparing, for each set of channel parameters, the measured CSI value and the predicted CSI value corresponding to the same set of channel parameters (see Fig.4 and para [0050] wherein a machine learning (ML) based (CSI prediction) model may be trained to learn relationships between measured CSI at a first time, measured CSI at a second time, and the time delay between the first time and the second time and CSI measurements being performed at the UE and reported to the gNB, is mentioned and also these measurements being added to a training data set that may be used to train (or re-train) the ML-based CSI prediction model, is mentioned and also see Fig.9, see para [0094] wherein the UE measuring CSI based on the reference signals 912 and predicting CSI at a future time using the prediction model, is mentioned & para [0095] wherein the UE 902 transmitting a message 920 including the predicted CSI and measured CSI reports to gNB 904 for use by the gNB to configure a qualifying scheme for the UE, is mentioned and also see para [0096] wherein the gNB determining whether the differences reported by the UE indicate that the respective CSI predictions are qualified or nonqualified predictions and after determining whether the predictions are qualified or nonqualified predictions, and gNB 904 generating the qualifying scheme configuration (that includes determining a prediction accuracy of the AI/ML feature), is mentioned).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the above method of Moderator_Qualcomm and LI et al. to include determining a prediction accuracy of the AI/ML feature by comparing, for each set of channel parameters, the measured CSI value and the predicted CSI value corresponding to the same set of channel parameters, disclosed by XUE et al. in order to provide an effective mechanism of a wireless device for efficiently predicting channel state information (CSI) using machine learning models and qualifying CSI predictions made using machine learning models and also efficiently reporting to o the network entity both the calculated CSI and the quantized CSI difference value in wireless communication system.
Allowable Subject Matter
5. Claims 22-30 and 32-40 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Madadi et al. (US Pub. No: 2022/0338189 A1) disclose mechanisms relating to application of machine learning in communications equipment, and more specifically to improved channel state information feedback.
Lo et al. (US Pub. No: 20240088968 A1) disclose mechanisms relating to a method and apparatus for support of machine learning (ML) or artificial intelligence (AI)-assisted channel state information (CSI) feedback in wireless communication system.
GUO (US Pub. No: 2024/0364405 A1) mechanisms relating to systems and methods for enabling machine learning based Channel State Information Reference Signal (CSI-RS) resource measurement and reporting in wireless communication system.
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/SRINIVASA R REDDIVALAM/Primary Examiner, Art Unit 2477 9/30/2026
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